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Image Captcha Solving Using TensorFlow and CNN Model. Accuracy 90%+

License: MIT License

Python 2.02% Jupyter Notebook 97.98%

captcha-solving captcha captcha-solver captcha-generator captcha-recognition captcha-breaking tensorflow cnn-model

captcha-tensorflow's Introduction

Captcha Solving Using TensorFlow

Introduction

Solve captcha using TensorFlow.
Learn CNN and TensorFlow by a practical project.

Follow the steps, run the code, and it works!

the accuracy of 4 digits version can be as high as 99.8%!

There are several more steps to put this prototype on production.

Ping me for paid technical supports.

[email protected]

Solve Captcha Using CNN Model
- Training: 4-digits Captcha
- Training: 4-letters Captcha
- Inference: load trained model and predict given images
Generate DataSet for Training
- Usage
- Example 1: 4 chars per captcha, use digits only
- Example 2: sampling random images

Solve Captcha Using CNN Model

old code that using tensorflow 1.x is moved to tensorflow_v1.

Training: 4-digits Captcha

this is a perfect project for beginers.

we will train a model of ~90% accuracy in 1 minute using one single GPU card (GTX 1080 or above).

if we increase the dataset by 10x, the accuracy increases to 98.8%. we can further increase the accuracy to 99.8% using 1M traning images.

here is the source code and running logs: captcha-solver-tf2-4digits-AlexNet-98.8.ipynb

Images, Ground Truth and Predicted Values:

there is 1 predicton error out of the 20 examples below. 9871 -> 9821

Accuracy and Loss History:

Model Structure:

3 convolutional layers, followed by 2x2 max pooling layer each.
1 flatten layer
2 dense layer

Training: 4-letters Captcha

this is a more practical project.

the code is the same as the 4-digits version, but the training dataset is much bigger.

it costs 2-3 hours to generate training dataset and costs 30 min to train a 95% accuracy model.

here is the source code and running logs: captcha-solver-tf2-4letters-AlexNet.ipynb

Inference: load trained model and predict given images

example: captcha-solver-model-restore.ipynb

Generate DataSet for Training

Usage

$ python datasets/gen_captcha.py  -h
usage: gen_captcha.py [-h] [-n N] [-c C] [-t T] [-d] [-l] [-u] [--npi NPI] [--data_dir DATA_DIR]

optional arguments:
  -h, --help           show this help message and exit
  -n N                 epoch number of character permutations.
  -c C                 max count of images to generate. default unlimited
  -t T                 ratio of test dataset.
  -d, --digit          use digits in dataset.
  -l, --lower          use lowercase in dataset.
  -u, --upper          use uppercase in dataset.
  --npi NPI            number of characters per image.
  --data_dir DATA_DIR  where data will be saved.

examples:

Example 1: 4 chars per captcha, use digits only

1 epoch has 10*9*8*7=5040 images, generate 6 epoches for training.

generating the dataset:

$ python datasets/gen_captcha.py -d --npi=4 -n 6
10 choices: 0123456789
generating 6 epoches of captchas in ./images/char-4-epoch-6/train
generating 1 epoches of captchas in ./images/char-4-epoch-6/test
write meta info in ./images/char-4-epoch-6/meta.json

preview the dataset:

$ python datasets/base.py images/char-4-epoch-6/
========== Meta Info ==========
num_per_image: 4
label_choices: 0123456789
height: 100
width: 120
n_epoch: 6
label_size: 10
==============================
train images: (30240, 100, 120), labels: (30240, 40)
test images: (5040, 100, 120), labels: (5040, 40)

Example 2: sampling random images

scenario: use digits/upper cases, 4 chars per captcha image.

1 epoch will have 36*35*34*33=1.4M images. the dataset is too big to debug.

using -c 10000 param, sampling 10k random images.

generating the dataset:

$ python3 datasets/gen_captcha.py -du --npi 4 -n 1 -c 10000
36 choices: 0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ
generating 1 epoches of captchas in ./images/char-4-epoch-1/train.
only 10000 records used in epoche 1. epoche_count: 1413720

Running Jupyter in docker

tensorflow image: https://hub.docker.com/r/jackon/tensorflow-2.1-gpu

docker pull jackon/tensorflow-2.1-gpu
# check if gpu works in docker container
docker run --rm --gpus all -t jackon/tensorflow-2.1-gpu /usr/bin/nvidia-smi
# start jupyter server in docker container
docker run --rm --gpus all -p 8899:8899 -v $(realpath .):/tf/notebooks -t jackon/tensorflow-2.1-gpu

captcha-tensorflow's People

Contributors

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captcha-tensorflow's Issues

[paraphrased] How to make the model support different number of characters per image?

Specific question No issue.

Hello There,

First of All Nice job!
I have a specific question, i am looking for a captcha solver
That can solve the Pictures i provide aa attachment,

I am looking for almost 7 months Now And still dont found
Where i looking for, i want to use the solver in Java,

I hope their is Someone that can help, sorry for opening the issue
I dont know How to contact on a other way.

Greetings Klapkaak078

Why not the training accuracy and test accuracy rise?

我是直接使用的此代码，其中只是更改了数据集的大小，设定的数据集为30000张训练集，6000张测试集，每幅验证码由数字和大小写字符构成。但是实际在训练时发现train accuracy 和test accuracy都上不去，而且loss达到4.12左右就不再下降，请问这是什么原因？

what version of python to use?

ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape

#python cnn_1_char.py --data_dir=images/char-1-epoch-1000/

data loaded
train images: 10000. test images: 2000
label_size: 10, image_size: 6000
2018-03-18 15:52:23.154212: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2018-03-18 15:52:23.246932: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:892] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-03-18 15:52:23.247218: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 0 with properties: 
name: GeForce GTX 1050 major: 6 minor: 1 memoryClockRate(GHz): 1.493
pciBusID: 0000:01:00.0
totalMemory: 3.95GiB freeMemory: 2.73GiB
2018-03-18 15:52:23.247233: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 1050, pci bus id: 0000:01:00.0, compute capability: 6.1)
2018-03-18 15:52:34.593156: W tensorflow/core/common_runtime/bfc_allocator.cc:273] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.43GiB.  Current allocation summary follows.
2018-03-18 15:52:34.593232: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (256):   Total Chunks: 31, Chunks in use: 31. 7.8KiB allocated for chunks. 7.8KiB in use in bin. 2.1KiB client-requested in use in bin.
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2018-03-18 15:52:34.593436: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (131072):        Total Chunks: 4, Chunks in use: 4. 800.0KiB allocated for chunks. 800.0KiB in use in bin. 800.0KiB client-requested in use in bin.
2018-03-18 15:52:34.593453: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (262144):        Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
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2018-03-18 15:52:34.593578: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (33554432):      Total Chunks: 2, Chunks in use: 1. 93.75MiB allocated for chunks. 45.78MiB in use in bin. 45.78MiB client-requested in use in bin.
2018-03-18 15:52:34.593599: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (67108864):      Total Chunks: 4, Chunks in use: 4. 375.00MiB allocated for chunks. 375.00MiB in use in bin. 375.00MiB client-requested in use in bin.
2018-03-18 15:52:34.593616: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (134217728):     Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-03-18 15:52:34.593636: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (268435456):     Total Chunks: 1, Chunks in use: 1. 1.98GiB allocated for chunks. 1.98GiB in use in bin. 1.43GiB client-requested in use in bin.
2018-03-18 15:52:34.593654: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin for 1.43GiB was 256.00MiB, Chunk State: 
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2018-03-18 15:52:34.594624: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 1 Chunks of size 80128 totalling 78.2KiB
2018-03-18 15:52:34.594639: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 4 Chunks of size 204800 totalling 800.0KiB
2018-03-18 15:52:34.594655: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 1 Chunks of size 48000000 totalling 45.78MiB
2018-03-18 15:52:34.594671: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 4 Chunks of size 98304000 totalling 375.00MiB
2018-03-18 15:52:34.594686: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 1 Chunks of size 2126722304 totalling 1.98GiB
2018-03-18 15:52:34.594701: I tensorflow/core/common_runtime/bfc_allocator.cc:683] Sum Total of in-use chunks: 2.39GiB
2018-03-18 15:52:34.594721: I tensorflow/core/common_runtime/bfc_allocator.cc:685] Stats: 
Limit:                  2619473920
InUse:                  2569063936
MaxInUse:               2569063936
NumAllocs:                     189
MaxAllocSize:           2126722304

2018-03-18 15:52:34.594755: W tensorflow/core/common_runtime/bfc_allocator.cc:277] ******_***********************************************************************xxxxxxxxxxxxxxxxxxxxxx
2018-03-18 15:52:34.594788: W tensorflow/core/framework/op_kernel.cc:1192] Resource exhausted: OOM when allocating tensor with shape[2000,32,100,60]
Traceback (most recent call last):
  File "cnn_1_char.py", line 167, in <module>
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 48, in run
    _sys.exit(main(_sys.argv[:1] + flags_passthrough))
  File "cnn_1_char.py", line 148, in main
    test_summary, test_accuracy = sess.run([merged, accuracy], feed_dict={x: test_x, y_: test_y, keep_prob: 1.0})
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 889, in run
    run_metadata_ptr)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1120, in _run
    feed_dict_tensor, options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1317, in _do_run
    options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1336, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[2000,32,100,60]
         [[Node: convolution-layer-1/Conv2D = Conv2D[T=DT_FLOAT, data_format="NHWC", padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](input/Reshape, convolution-layer-1/Variable/read)]]

Caused by op u'convolution-layer-1/Conv2D', defined at:
  File "cnn_1_char.py", line 167, in <module>
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 48, in run
    _sys.exit(main(_sys.argv[:1] + flags_passthrough))
  File "cnn_1_char.py", line 75, in main
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
  File "cnn_1_char.py", line 41, in conv2d
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 631, in conv2d
    data_format=data_format, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2956, in create_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1470, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[2000,32,100,60]
         [[Node: convolution-layer-1/Conv2D = Conv2D[T=DT_FLOAT, data_format="NHWC", padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](input/Reshape, convolution-layer-1/Variable/read)]]

How can I solve it?

How to test using a single image

Hey, i found this code and it's amazing, thanks for all! I have a issue... how to pass a single captcha per time? In the example you pass a batch.

plus issue: how can i gerenate a black and white captcha? In this case you pass the number 3 on the argument C (RGB), i need to generate more collored type of captchas.

is possible resolve this?

https://github.com/aero2a/kape-

I can found php library. Is possible next using C code to resolve after training net?

how should i adpat to a different image size?

there are some errors indicates me that image size doesn't fit

Invalid argument: Input to reshape is a tensor with 832000 values, but the requested shape requires a multiple of 40000

original size generated from captcha was 100100, could you please tell me how to adpat to a new size, for example, my image size is 20050

train datesets accuracy is very low,why?

train count: 14817, valid count: 6351, test count: 9072

how to deal with it??

这项目基于什么环境运行起来

github上的Tensorflow相关项目，有哪个可以直接运用到实际应用中的？

目前急切想实现Tensorflow的实际应用和运用？
求推荐，文档全，基本能顺利跑起来的

AttributeError: 'module' object has no attribute 'load_data_1char'

I try your code, but encounter an error while execute softmax_with_log.py. any help?
thanks

[sumarsono@mesin-arch captcha-tensorflow]$ python2 softmax_with_log.py 
Traceback (most recent call last):
  File "softmax_with_log.py", line 115, in <module>
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
  File "/home/sumarsono/captcha-tensorflow/virt/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 48, in run
    _sys.exit(main(_sys.argv[:1] + flags_passthrough))
  File "softmax_with_log.py", line 37, in main
    train_data, test_data = input_data.load_data_1char(FLAGS.data_dir)
AttributeError: 'module' object has no attribute 'load_data_1char'

TypeError: 'float' object cannot be interpreted as an integer

Nice project. Thanks for sharing. Having problem though. Cloned repo. Did a pip install -r requirements.txt. Tried to run gen_captcha.py and I get a TypeError.

I uncommented the tensorflow=2.1.4 line in the requirements.txt file and set it to 2.8.4 since 2.1.4 was not found and the earliest version of tensroflow was 2.8.4.

My env:
MacOS Monteray v12.6
Python 3.10.7

(.env) ➜  captcha-tensorflow git:(master) python datasets/gen_captcha.py -d --npi=4 -n 6
10 choices: 0123456789
generating 6 epoches of captchas in ./images/char-4-epoch-6/train.
Traceback (most recent call last):
  File "/Users/evancarey/prj/captcha-tensorflow/datasets/gen_captcha.py", line 137, in <module>
    gen_dataset()
  File "/Users/evancarey/prj/captcha-tensorflow/datasets/gen_captcha.py", line 80, in gen_dataset
    _gen_captcha(build_file_path('train'), num_per_image, n_epoch, width, height, choices=choices, max_images_count=max_images_count)
  File "/Users/evancarey/prj/captcha-tensorflow/datasets/gen_captcha.py", line 48, in _gen_captcha
    image.write(captcha, fn)
  File "/Users/evancarey/prj/captcha-tensorflow/.env/lib/python3.10/site-packages/captcha/image.py", line 58, in write
    im = self.generate_image(chars)
  File "/Users/evancarey/prj/captcha-tensorflow/.env/lib/python3.10/site-packages/captcha/image.py", line 228, in generate_image
    im = self.create_captcha_image(chars, color, background)
  File "/Users/evancarey/prj/captcha-tensorflow/.env/lib/python3.10/site-packages/captcha/image.py", line 212, in create_captcha_image
    mask = im.convert('L').point(table)
  File "/Users/evancarey/prj/captcha-tensorflow/.env/lib/python3.10/site-packages/PIL/Image.py", line 1651, in point
    return self._new(self.im.point(lut, mode))
TypeError: 'float' object cannot be interpreted as an integer
(.env) ➜  captcha-tensorflow git:(master)

conv_captcha.py里是只对验证码一个字符进行识别？

    # forward prop
    predict = tf.argmax(y_conv, axis=1)
    expect = tf.argmax(y_, axis=1)

    # evaluate accuracy
    with tf.name_scope('evaluate_accuracy'):
        correct_prediction = tf.equal(predict, expect)
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
        variable_summaries(accuracy)

如果要改成识别四个字符的验证码，应该怎么改呢

problem facing in 6 digit captcha read

I'm unable to run the code in 6 digit captcha break.

TypeError: run() takes no arguments (2 given)

$ python simple_softmax.py images/char-1-epoch-2000/

Traceback (most recent call last):
File "simple_softmax.py", line 70, in
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
TypeError: run() takes no arguments (2 given)

Forced exit task while generating the dataset

I tried to generate datasets with 6 letters as follow:

!python3 datasets/gen_captcha.py -dul --npi 6 -n 1 -c 10000 --data_dir ./gdrive/MyDrive/Teledrop-Datasets/

But it turns that the process in google colab exited automatically, because of the usage of the RAM. Is there any way to limit the RAM usage?

Improved training (train a model in TF to solve capchat)

If you allow me,

here is a good tutorial on how to train a model in TF to solve capchat.

https://github.com/m-a-r-o-u/coursera-tensorflow2_for_deeplearning_specialization/blob/main/C1-Getting_Started_With_TensorFlow_2/C1-W5-Capstone_Project/C1-Colab-Capstone_Project_SVHN.ipynb

验证码不用灰度化降噪去干扰线之类的处理么，我的电脑训练四个字符的时候卡死了好奇怪

How to make the model support different number of characters per image?

How do I test the model in tensorflow.js (node-js)?

def format_y(y):
    return ''.join(map(lambda x: chr(int(x)), y))

def predict(image_path):
    im = Image.open(image_path)
    # im = im.resize((H, W))
    im = np.array(im) / 255.0
    im = np.array(im)
    
    y_pred = model.predict(np.array([im]))
    y_pred = tf.math.argmax(y_pred, axis=-1)
    
    print('predict: %s' % format_y(y_pred[0]))
    plt.imshow(im)

Im struggling to input the image in tensorflowjs, it always throw error ValueError: Error when checking model : the Array of Tensors that you are passing to your model is not the size the the model expected model expected. Expected to see 1 Tensor(s), but instead got 0 Tensors(s).

After Training

What command do you run after you've trained it to test it on the actual image that i'm trying to solve.

I followed your code to learn how to build a program but I have stuck about file meta.json. Can you give me format about meta.json file?

ok, i run it, what next?

What I can to do to solve my image?

I have image and need get string to resolve captcha, where is code?

Is possible to tell what is format of data to train and test?
filename is only 'string'_some.png
some will be everything?

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